Field-scale evaluation of a satellite-based terrestrial biosphere model for estimating crop response to management practices and productivity

Timely and accurate information on crop productivity is essential for characterizing crop growing status and guiding adaptive management practices to ensure food security. Terrestrial biosphere models forced by satellite observations (satellite-TBMs) are viewed as robust tools for understanding large-scale agricultural productivity, with distinct advantages of generalized input data requirement and comprehensive representation of carbon-- water-energy exchange mechanisms. However, it remains unclear whether these models can maintain consistent accuracy at field scale and provide useful information for farmers to make site-specific management decisions. This study aims to investigate the capability of a satellite-TBM to estimate crop productivity at the granularity of individual fields using harmonized Sentinel-2 and Landsat-8 time series. Emphasis was placed on evaluating the model performance in: (i) representing crop response to the spatially and temporally varying field management practices, and (ii) capturing the variation in crop growth, biomass and yield under complex interactions among crop genotypes, environment, and management conditions. To achieve the first objective, we conducted on-farm experiments with controlled nitrogen (N) fertilization and irrigation treatments to assess the efficacy of using satellite-retrieved leaf area index (LAI) to reflect the effect of management practices in the TBM. For the second objective, we integrated a yield formation module into the satellite-TBM and compared it with the semi-empirical harvest index (HI) method. The model performance was then evaluated under varying conditions using an extensive dataset consisting of observations from four crop species (i.e., soybean, wheat, rice and maize), 42 cultivars and 58 field-years. Results demonstrated that satellite-retrieved LAI effectively captured the effects of N and water supply on crop growth, showing high sensitivity to both the timing and quantity of these inputs. This allowed for a spatiotemporal representation of management impacts, even without prior knowledge of the specific management schedules. The TBM forced by satellite LAI produced consistent biomass dynamics with ground measurements, showing an overall correlation coefficient (R) of 0.93 and a relative root mean square error (RRMSE) of 31.4 %. However, model performance declined from biomass to yield estimation, with the HI- based method (R = 0.80, RRMSE = 23.7 %) outperforming mechanistic modeling of grain filling (R = 0.43, RRMSE = 43.4 %). Model accuracy for winter wheat was lower than that for summer crops such as rice, maize and soybean, suggesting potential underrepresentation of the overwintering processes. This study illustrates the utility of satellite-TBMs in crop productivity estimation at the field level, and identifies existing uncertainties and limitations for future model developments.